Alcohol consumption, depressive symptoms, and the incidence of diabetes‐related complications
Bibliographic record
Abstract
BACKGROUND: Heavy alcohol consumption in individuals with type 2 diabetes mellitus (T2DM) is related to increased risks of diabetes-related micro- and macrovascular complications. Depressive symptoms may be relevant to this relationship, because high depressive symptoms are associated with an increased risk of complications. This study investigated whether the interaction between depressive symptoms and alcohol frequency was positively related to the development of neuropathy, retinopathy, nephropathy, and coronary artery disease (CAD), such that those with high depressive symptoms and high alcohol frequency will be at increased risk of complications. METHODS: Data were from five waves of the Evaluation of Diabetes Treatment annual survey including 1413 adults with T2DM in Quebec. Data on alcohol frequency (number of drinking occasions), depressive symptoms, and complications were collected annually. The development of each complication was investigated using multiple logistic regression analysis with generalized estimating equations. RESULTS: After adjusting for sociodemographic, lifestyle, and diabetes-related covariates, the interaction between alcohol frequency and depressive symptoms was positively related to the incidence of neuropathy and CAD, such that those with high depressive symptoms who drank the most frequently had the highest risk of neuropathy (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.00-1.04; P = 0.04) and CAD (OR 1.02, 95% CI 1.00-1.04; P = 0.04). This interaction was not significantly related to retinopathy or nephropathy. CONCLUSION: Individuals with high depressive symptoms and high alcohol frequency may have a particularly high risk of neuropathy and CAD. Future prevention efforts should examine both alcohol frequency and depressive symptoms when evaluating the risk of complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".